The acronyms are expanded using the acronym database, so the acronyms are also matched with the expanded acronyms, and entailment is predicted accordingly

Acronym guide + UAIC_Acronym_rules

UAIC20091.3way

0.17

0.16

We start from acronym-guide, but additional we use a rule that consider for expressions like Xaaaa Ybbbb Zcccc the acronym XYZ, regardless of length of text with this form.

DIRT

BIU1.2way

1.33

—

Inference rules

DIRT

Boeing3.3way

-1.17

0

Verb paraphrases

DIRT

UAIC20091.3way

0.17

0.33

We transform text and hypothesis with MINIPAR into dependency trees: use of DIRT relations to map verbs in T with verbs in H

Framenet+ WordNet

DLSIUAES1.2way

1.16

—

Frame-to-frame similarity metric

Framenet+ WordNet

DLSIUAES1.3way

-0.17

-0.17

Frame-to-frame similarity metric

Framenet

UB.dmirg3.2way

0

—

If two lexical items are covered in a single FrameNet frame, then the two items are treated as semantically related.

Grady Ward’s MOBY Thesaurus + Roget's Thesaurus

VensesTeam2.2way

2.83

—

Semantic fields are used as semantic similarity matching, in all cases of non identical lemmas

MontyLingua Tool

Siel_093.3way

0

0

For the VerbOcean, the verbs have to be in the base form. We used the "MontyLingua" tool to convert the verbs into their base form

NEGATION_rules by UAIC

UAIC20091.3way

0

-1.34

Negation rules check in the dependency trees on verbs descending branches to see if some categories of words that change the meaning are found.

NER (RASP Parser nertag)

JU_CSE_TAC1.2way

0

—

Named Entity match: measure based on the number of Nes in the hypothesis that match in the corresponding text. For named entity recognition, the RASP Parser (Briscoe et al., 2006) nertag component has been used.

NE component

UI_ccg1.2way

4.83

—

Named Entity recognition/comparison

PropBank

cswhu1.3way

2

3.17

syntactic and semantic parsing

Stanford NER

QUANTA1.2way

0.67

—

We use Named Entity similarity as a feature

Stopword list

FBKirst1.2way

1.5

—

A list of the 572 most frequent English words has been collected in order to prevent assigning high costs to the deletion/insertion of terms that are unlikely to bring relevant information to detect entailment,and to avoid substituting these terms with any content word.

Training data from RTE1, 2, 3

PeMoZa3.2way

0

—

Training data from RTE2

PeMoZa3.2way

0.66

—

Training data from RTE2, 3

PeMoZa3.2way

0

—

VerbOcean

DFKI1.3way

0

0.17

VerbOcean relations are used to calculate relatedness between verbs in T and H

VerbOcean

DFKI2.3way

0.33

0.5

VerbOcean relations are used to calculate relatedness between verbs in T and H

VerbOcean

DFKI3.3way

0.17

0.17

VerbOcean relations are used to calculate relatedness between verbs in T and H

VerbOcean

FBKirst1.2way

-0.16

—

Extraction of 18232 entailment rules for all the English verbs connected by the ”stronger-than” relation. For instance, if ”kill [stronger-than] injure”, then the rule ”kill ENTAILS injure” is added to the rules repository.

NE module: NERs, in order to identify Persons, Locations, Jobs, Languages, etc; Perl patterns built by us for RTE4 in order to identify numbers and dates; our own resources extracted from Wikipedia in order to identify a "distance" between one name entity from hypothesis and name entities from text

WordNet

AUEBNLP1.3way

-2

-2.67

Synonyms

WordNet

BIU1.2way

2.5

—

Synonyms, hyponyms (2 levels away from the original term), hyponym_instance and derivations

WordNet based Unigram match: if any synset for the H unigram matches with any synset of a word in T then the hypothesis unigram is considered as a WordNet based unigram match.

WordNet

PeMoZa1.2way

-0.5

—

Derivational Morphology from WordNet

WordNet

PeMoZa1.2way

1.33

—

Verb Entailment from Wordnet

WordNet

PeMoZa2.2way

1

—

Derivational Morphology from WordNet

WordNet

PeMoZa2.2way

-0.33

—

Verb Entailment from Wordnet

WordNet

QUANTA1.2way

-0.17

—

We use several relations from wordnet, such as synonyms, hyponym, hypernym et al.

WordNet

Rhodes.3way

3.17

4

Lexicon based match: we chose

a very simple metric: matching between words in T and H based on a path of distance at most 2 in the
WordNet graph, using any links (hyponymy, hypernymy, meronymy, pertainymy, etc.)

WordNet

Sagan1.3way

0

-0.83

The system is based on machine learning approach. The ablation test was obtained with 2 less features using WordNet (namely, string similarity based on Levenshtein distance and semantic similarity) in the training and testing steps.

WordNet

Siel_093.3way

0.34

-0.17

Similarity between nouns using WN tool

WordNet

ssl1.3way

0

0.67

WordNet Analysis

WordNet

UB.dmirg3.2way

0

—

Synonyms, hypernyms (2 levels away from the original term)

WordNet

UI_ccg1.2way

4

—

Word similarity == identity

WordNet +FrameNet

UB.dmirg3.2way

0

—

WN: synonyms, hypernyms (2 levels away from the original term). FN: if two lexical items are covered in a single FrameNet frame, then the two items are treated as semantically related.

WordNet +VerbOcean

DFKI1.3way

0

0.17

VerbOcean is used to calculate relatedness between nominal predicates in T and H, after using WordNet to change the nouns into verbs.

WordNet +VerbOcean

DFKI2.3way

0.5

0.67

VerbOcean is used to calculate relatedness between nominal predicates in T and H, after using WordNet to change the nouns into verbs.

WordNet +VerbOcean

DFKI3.3way

0.17

0.17

VerbOcean is used to calculate relatedness between nominal predicates in T and H, after using WordNet to change the nouns into verbs.